contact_nb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21da7e65",
   "metadata": {},
   "source": [
    "# The FDR contact list\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96a0ebc",
   "metadata": {},
   "source": [
    "## 0. set-up. necessary files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "40611845",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "512bda53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the path to your Excel file\n",
    "file_path = \"data/fake_contact_list.xls\"\n",
    "# Use the read_excel function to read the Excel file into a DataFrame\n",
    "df = pd.read_excel(file_path); df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24d07272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09</td>\n",
       "      <td>05</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09</td>\n",
       "      <td>11</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>06</td>\n",
       "      <td>02</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09</td>\n",
       "      <td>20</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>09</td>\n",
       "      <td>22</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>07</td>\n",
       "      <td>08</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  day  year\n",
       "0    NaN  NaN  2000\n",
       "1    NaN  NaN  2000\n",
       "2    NaN  NaN  2000\n",
       "3    NaN  NaN  2000\n",
       "4    NaN  NaN  2000\n",
       "5     09   05  2000\n",
       "6     09   11  2000\n",
       "7     06   02  2000\n",
       "8     09   20  2000\n",
       "9     10   19  2000\n",
       "10    09   22  2000\n",
       "11    07   08  2000\n",
       "12   NaN  NaN  2000"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the month, day, and year\n",
    "df['month'] = df['Birthday'].str[:2]\n",
    "df['day'] = df['Birthday'].str[2:4]\n",
    "df['year'] = '2000'\n",
    "\n",
    "# Create a new DataFrame with the extracted columns\n",
    "new_df = df[['month', 'day', 'year']]; new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "abf3c393",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bdays = pd.to_datetime(df[\"Birthday\"])\n",
    "bdays = pd.to_datetime(new_df, errors = 'coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc0c3a1",
   "metadata": {},
   "source": [
    "Now, we're comparing the gathered dates with the current dates to get all birthdays during the same month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "06dbd8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the current month and year\n",
    "current_date = datetime.now()\n",
    "current_month = current_date.month\n",
    "current_year = current_date.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c76c39d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaT\n",
       "1           NaT\n",
       "2           NaT\n",
       "3           NaT\n",
       "4           NaT\n",
       "5    2000-09-05\n",
       "6    2000-09-11\n",
       "7    2000-06-02\n",
       "8    2000-09-20\n",
       "9    2000-10-19\n",
       "10   2000-09-22\n",
       "11   2000-07-08\n",
       "12          NaT\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdf1bb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Filter the DataFrame to get rows with the same month as the present time\n",
    "same_month_rows = df[bdays.dt.month == current_month]; same_month_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e51d2382",
   "metadata": {},
   "outputs": [],
   "source": [
    "names_str = ', '.join( list(same_month_rows[\"First Name\"]) )\n",
    "month_data = same_month_rows[[\"First Name\", \"Last Name\", \"day\", \"month\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e2c5ea06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pablo</td>\n",
       "      <td>Zorrilla</td>\n",
       "      <td>05</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hugo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Diego</td>\n",
       "      <td>Felgueres</td>\n",
       "      <td>20</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ana Beatriz</td>\n",
       "      <td>Souza</td>\n",
       "      <td>22</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     First Name  Last Name day month\n",
       "5         Pablo   Zorrilla  05    09\n",
       "6          Hugo        NaN  11    09\n",
       "8         Diego  Felgueres  20    09\n",
       "10  Ana Beatriz      Souza  22    09"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96eeb65",
   "metadata": {},
   "outputs": [],
   "source": [
    "names_str"
   ]
  }
 ],
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